scholarly journals Neonatal sepsis prediction through clinical decision support algorithms – a systematic review

2021 ◽  
Author(s):  
Emma Persad ◽  
Kerstin Jost ◽  
Antoine Honoré ◽  
David Forsberg ◽  
Karen Coste ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Neonatal Sepsis ◽  
Clinical Decision

scholarly journals The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance: a systematic review and meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sharare Taheri Moghadam ◽  
Farahnaz Sadoughi ◽  
Farnia Velayati ◽  
Seyed Jafar Ehsanzadeh ◽  
Shayan Poursharif
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Patient Outcomes ◽  
Clinical Decision Support ◽  
Meta Analysis ◽  
Clinical Decision ◽  
Cochrane Library ◽  
Physician Practice ◽  
Positive Effects ◽  
Practice Performance

Abstract Background Clinical Decision Support Systems (CDSSs) for Prescribing are one of the innovations designed to improve physician practice performance and patient outcomes by reducing prescription errors. This study was therefore conducted to examine the effects of various CDSSs on physician practice performance and patient outcomes. Methods This systematic review was carried out by searching PubMed, Embase, Web of Science, Scopus, and Cochrane Library from 2005 to 2019. The studies were independently reviewed by two researchers. Any discrepancies in the eligibility of the studies between the two researchers were then resolved by consulting the third researcher. In the next step, we performed a meta-analysis based on medication subgroups, CDSS-type subgroups, and outcome categories. Also, we provided the narrative style of the findings. In the meantime, we used a random-effects model to estimate the effects of CDSS on patient outcomes and physician practice performance with a 95% confidence interval. Q statistics and I2 were then used to calculate heterogeneity. Results On the basis of the inclusion criteria, 45 studies were qualified for analysis in this study. CDSS for prescription drugs/COPE has been used for various diseases such as cardiovascular diseases, hypertension, diabetes, gastrointestinal and respiratory diseases, AIDS, appendicitis, kidney disease, malaria, high blood potassium, and mental diseases. In the meantime, other cases such as concurrent prescribing of multiple medications for patients and their effects on the above-mentioned results have been analyzed. The study shows that in some cases the use of CDSS has beneficial effects on patient outcomes and physician practice performance (std diff in means = 0.084, 95% CI 0.067 to 0.102). It was also statistically significant for outcome categories such as those demonstrating better results for physician practice performance and patient outcomes or both. However, there was no significant difference between some other cases and traditional approaches. We assume that this may be due to the disease type, the quantity, and the type of CDSS criteria that affected the comparison. Overall, the results of this study show positive effects on performance for all forms of CDSSs. Conclusions Our results indicate that the positive effects of the CDSS can be due to factors such as user-friendliness, compliance with clinical guidelines, patient and physician cooperation, integration of electronic health records, CDSS, and pharmaceutical systems, consideration of the views of physicians in assessing the importance of CDSS alerts, and the real-time alerts in the prescription.

Computerized Clinical Decision Support Systems and Antibiotic Prescribing: A Systematic Review and Meta-analysis

2019 ◽  
Vol 41 (3) ◽  
pp. 552-581 ◽  
Author(s):  
Eduardo Carracedo-Martinez ◽  
Christian Gonzalez-Gonzalez ◽  
Antonio Teixeira-Rodrigues ◽  
Jesus Prego-Dominguez ◽  
Bahi Takkouche ◽  
...  
Keyword(s):  
Systematic Review ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Meta Analysis ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Antibiotic Prescribing

scholarly journals Clinical Decision Support Systems in Breast Cancer: A Systematic Review

Cancers ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 369 ◽  
Author(s):  
Claudia Mazo ◽  
Cathriona Kearns ◽  
Catherine Mooney ◽  
William M. Gallagher
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Major Advance

Breast cancer is the most frequently diagnosed cancer in women, with more than 2.1 million new diagnoses worldwide every year. Personalised treatment is critical to optimising outcomes for patients with breast cancer. A major advance in medical practice is the incorporation of Clinical Decision Support Systems (CDSSs) to assist and support healthcare staff in clinical decision-making, thus improving the quality of decisions and overall patient care whilst minimising costs. The usage and availability of CDSSs in breast cancer care in healthcare settings is increasing. However, there may be differences in how particular CDSSs are developed, the information they include, the decisions they recommend, and how they are used in practice. This systematic review examines various CDSSs to determine their availability, intended use, medical characteristics, and expected outputs concerning breast cancer therapeutic decisions, an area that is known to have varying degrees of subjectivity in clinical practice. Utilising the methodology of Kitchenham and Charter, a systematic search of the literature was performed in Springer, Science Direct, Google Scholar, PubMed, ACM, IEEE, and Scopus. An overview of CDSS which supports decision-making in breast cancer treatment is provided along with a critical appraisal of their benefits, limitations, and opportunities for improvement.

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